Adverse drug reactions (ADRs) are a common problem in clinical and pharmacovigilance research and can lead to serious patient harm and biased conclusions if modelled poorly. Sparse, noisy, and highly imbalanced drug ADR data often cause standard machine learning methods to perform no better than naïve frequency‑based approaches, unless appropriate low‑rank and kernel‑based methods are used with clear assumptions [1].
The primary source of datasets are DGIdb 4.0, SIDER 4.1 and PubChem database.
=======The primary source of data are DGIdb 4.0, SIDER 4.1 and PubChem database.
>>>>>>> d63ef35872443e689156e15c694db1783f55380dThe above graph mentions the drugs majority of the drugs have more than 200 side effects, emphasising the importance of this study.
From the dataset, these are the drugs which possess the most side effects displayed alongside the most common side effects.
The above graph mentions the drugs that has the most side effects.
The above graph depicts the side effect that was most common among the drugs.
Usually you want to have a nice table displaying some important results that you have calculated. In posterdown this is as easy as using the kable table formatting you are probably use to as per typical R Markdown formatting.
You can reference tables like so: Table 1. Some basic summaries of the dataset are below:
| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width |
|---|---|---|---|
| 5.1 | 3.5 | 1.4 | 0.2 |
| 4.9 | 3.0 | 1.4 | 0.2 |
| 4.7 | 3.2 | 1.3 | 0.2 |
| 4.6 | 3.1 | 1.5 | 0.2 |
| 5.0 | 3.6 | 1.4 | 0.2 |
| 5.4 | 3.9 | 1.7 | 0.4 |
| 4.6 | 3.4 | 1.4 | 0.3 |
| 5.0 | 3.4 | 1.5 | 0.2 |
| 4.4 | 2.9 | 1.4 | 0.2 |
| 4.9 | 3.1 | 1.5 | 0.1 |
Figure 1, and Figure ?? below show the patterns in our dataset. Make sure that all the details in your plots will be legible when printed (legend text, axis text, and any labels)
Figure 1: Early Performance of ADR Prediction Methods
>>>>>>> d63ef35872443e689156e15c694db1783f55380d1.We implemented five ADR profile prediction methods using DGI features only: Naïve frequency model (ADR prevalence per column), kernel regression (KR), linear SVM, RBF‑kernel SVM, and VKR (NMF + kernel ridge regression).
Preliminary analysis on the DGI dataset shows that the Naïve baseline and VKR achieve the highest AUROC (≈0.91), while KR and VKR achieve the best AUPR (≈0.41–0.42), clearly outperforming SVM variants on both metrics. VKR therefore provides the best overall trade‑off between discrimination (AUROC) and rare ADR detection (AUPR), motivating its use as the main reference method in further experiments.
We plan to conduct further analysis using:
We will use the PYTHON Programming for this.
The code and datasets for this project can be viewed at our GitHub repository here: https://github.com/arshad4387/ADR-Prediction.git
<<<<<<< HEADWe would like to thank Dr. Yezhao and Dr. Haixuan for their and sharing the code and data through the github page.
======= >>>>>>> d63ef35872443e689156e15c694db1783f55380d[1] Zhong, Y., Seoighe, C., & Yang, H. (2024). Non-Negative matrix factorization combined with kernel regression for the prediction of adverse drug reaction profiles. Bioinformatics Advances, 4(1), vbae009.